{"id":"https://openalex.org/W3123665962","doi":"https://doi.org/10.1109/globecom42002.2020.9322390","title":"Anomaly Detection via Controlled Sensing and Deep Active Inference","display_name":"Anomaly Detection via Controlled Sensing and Deep Active Inference","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3123665962","doi":"https://doi.org/10.1109/globecom42002.2020.9322390","mag":"3123665962"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012419698","display_name":"Geethu Joseph","orcid":"https://orcid.org/0000-0002-5289-5403"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Geethu Joseph","raw_affiliation_strings":["Syracuse University, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101634809","display_name":"Chen Zhong","orcid":"https://orcid.org/0000-0003-3934-4436"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Zhong","raw_affiliation_strings":["Syracuse University, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076753154","display_name":"M. Cenk Gursoy","orcid":"https://orcid.org/0000-0002-7352-1013"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Cenk Gursoy","raw_affiliation_strings":["Syracuse University, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004337702","display_name":"Senem Velipasalar","orcid":"https://orcid.org/0000-0002-1430-1555"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Senem Velipasalar","raw_affiliation_strings":["Syracuse University, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018292481","display_name":"Pramod K. Varshney","orcid":"https://orcid.org/0000-0003-4504-5088"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pramod K. Varshney","raw_affiliation_strings":["Syracuse University, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":0.8227,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6965154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11883","display_name":"Embodied and Extended Cognition","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7057826519012451},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6352133750915527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6153267025947571},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4928760528564453},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3236086368560791}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7057826519012451},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6352133750915527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6153267025947571},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4928760528564453},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3236086368560791}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G3386215752","display_name":null,"funder_award_id":"1618615,1739748,1816732,DE-AR0000940","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1990041537","https://openalex.org/W1990503235","https://openalex.org/W2045030310","https://openalex.org/W2059320470","https://openalex.org/W2082680528","https://openalex.org/W2170341113","https://openalex.org/W2552810632","https://openalex.org/W2743681928","https://openalex.org/W2743911451","https://openalex.org/W2892266804","https://openalex.org/W2896121142","https://openalex.org/W3009317244","https://openalex.org/W3017302611","https://openalex.org/W3046947688","https://openalex.org/W3099581246","https://openalex.org/W4232896127","https://openalex.org/W7025045700"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"In":[0,80],"this":[1,25,81],"paper,":[2],"we":[3,83,177],"address":[4],"the":[5,10,15,27,46,54,64,77,101,112,116,119,145,152,156,159,164,182,193],"anomaly":[6],"detection":[7],"problem":[8],"where":[9],"objective":[11],"is":[12,57,127,133],"to":[13,62,74,93,99,137,143],"find":[14],"anomalous":[16,78],"processes":[17,34,65,92],"among":[18],"a":[19,31,41,69,85,107,134,169],"given":[20],"set":[21],"of":[22,33,45,72,122,147,158,196],"processes.":[23,79],"To":[24],"end,":[26],"decision-making":[28],"agent":[29,60],"probes":[30],"subset":[32],"at":[35,96],"every":[36,97],"time":[37],"instant":[38,98],"and":[39,118,162,191],"obtains":[40,68],"potentially":[42],"erroneous":[43],"estimate":[44],"binary":[47],"variable":[48],"which":[49,91,132],"indicates":[50],"whether":[51],"or":[52],"not":[53],"corresponding":[55],"process":[56],"anomalous.":[58],"The":[59],"continues":[61],"probe":[63],"until":[66],"it":[67],"sufficient":[70],"number":[71,121],"measurements":[73,123],"reliably":[75],"identify":[76],"context,":[82],"develop":[84],"sequential":[86,139],"selection":[87,160],"algorithm":[88,126,180],"that":[89],"decides":[90],"be":[94],"probed":[95],"detect":[100],"anomalies":[102],"with":[103,181],"an":[104],"accuracy":[105],"exceeding":[106],"desired":[108],"value":[109],"while":[110],"minimizing":[111],"delay":[113],"in":[114,141],"making":[115],"decision":[117],"total":[120],"taken.":[124],"Our":[125],"based":[128,185],"on":[129,186],"active":[130,165],"inference":[131,166],"general":[135],"framework":[136,167],"make":[138],"decisions":[140],"order":[142],"maximize":[144],"notion":[146],"free":[148,153],"energy.":[149],"We":[150],"define":[151],"energy":[154],"using":[155,168],"objectives":[157],"policy":[161],"implement":[163],"deep":[170,187],"neural":[171],"network":[172],"approximation.":[173],"Using":[174],"numerical":[175],"experiments,":[176],"compare":[178],"our":[179,197],"state-of-the-art":[183],"method":[184],"actor-critic":[188],"reinforcement":[189],"learning":[190],"demonstrate":[192],"superior":[194],"performance":[195],"algorithm.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
